Metabolite Profiling of Justicia gendarussa Burm. f. Leaves Using UPLC-UHR-QTOF-MS

An ultra-performance liquid chromatography ultra-high-resolution quadrupole-time-of-flight-mass spectrometry (UPLC-UHR-QTOF-MS) metabolite profiling ofxs Justicia gendarussa Burm. f. leaves was performed. PCA and HCA analyses were applied to observe the clustering patterns and inter-sample relationships. It seemed that the concentrations of Ca, P, and Cu in the soil could affect the metabolite profiles of Justicia gendarussa. Six significant metabolites were proposed.

Environmental factors, such as the site of cultivation, altitude, temperature, sun exposure time, rainfall, climate, and soil can influence the primary and secondary metabolites of plants [20][21][22]. These factors may affect secondary metabolites qualitatively and quantitatively, so their bioactivities could be varied [21][22][23]. Therefore, metabolite profiling studies of herbal plants is very important for ensuring their safety and efficacy.
LC-DAD-APCI-MS-based metabolite profiling of three species of Justicia, namely J. secunda, J. graciliflora, and J. refractifolia, had been reported [24]. To the best of our knowledge, there is no report written on any metabolite profiling of J. gendarussa originating from Indonesia before. In this work, UPLC-UHR-QTOF-MS was used to determine metabolite profiles of J. gendarussa leaves from different sites of cultivation (Table 1).

Tab. 1.
Origin of samples a

Results and Discussion
PCA analysis of pair RT and m/z ( Figure 1A) showed a definite discrimination of samples A, E, F, and G, whilst samples of B, C, and D were not well separated. The total variants explained by the three principle components (PC1, PC2, PC3) was 64.3%. In order to confirm the clusters observed in PCA, HCA analysis was also performed. A dendogram of all the samples ( Figure 2) indicated that samples were comprised of two clusters; cluster I consisted of samples (A, B, C, D), and cluster II was (E, F, G).

Fig. 1.
PCA score plot (A) and loading plot (B); explained variants PC1: 33.3%, PC2: 17.40%, and PC: 13.60%. Numbers (1-6) refer to significant metabolites as listed in Table 2 Tab The location of B was very close to C and D. Samples C and D were cultivated on the same location, but their plants' origins and soil types were different.
Two-way ANOVA showed significant differences between the concentrations of Ca, P, Cu, K, and Fe (p < 0.05) in soils at the sites of cultivation. Figure 3 showed significant positive correlation trends of the concentrations of Ca, P, and Cu in soils versus the metabolite profile's clustering of samples A, C, D, B, G, E, F (correlation coefficients were 0.771, 0.624, and 0.759, respectively; r table was 0.549 for p = 0.01). On the contrary, concentrations of Fe and K (data not shown) in soils did not yield any correlations with metabolite profiles of all samples (r = −0.382, and 0.041, respectively). It seemed that concentrations of Ca, P, and Cu could affect the metabolite profile of the samples, whilst concentrations of Fe and K in soils, altitude, average temperature, average rainfall, and climate type (Table 1) could not affect the profiles of the metabolites. PCA showed that samples B, C, and D could not be well-separated ( Figure 1A), although the concentrations of Ca, P, Cu, K, and Fe in the soils were significantly different (as described above). These indicated that other elements of the soils that were not determined in this work might also affect the metabolite profiles. Freitas et al. [25] reported the influence of soil nutrients (N, P, K, Ca, and B) on the secondary metabolite production in Passiflora alata.  (6) were previously reported for J. gendarussa leaves [15,16]. Metabolite (2) was reported as a demethylated product of prazosin in liver microsomes for rats, dogs, and humans [29].

Concentrations of calcium, phosphorus, and copper in soils
Tab. 3.

Proposed Compound
Ref.
(   (1), (2), and (5) were found in relatively high levels in samples E and F, whilst metabolites (3) and (4) were in sample G. The highest intensities of the metabolite (6) was found in sample F.

Fig. 5. Bucket statistic plots of significant metabolites
This study showed that metabolite profiles of J. gendarussa were affected by the plant's cultivation sites. To ensure the quality and efficacy of this medicinal plant, further studies are needed to compare its metabolites and bioactivity profiles.

Materials and Chemicals
J. gendarussa leaves were collected from Pacet, Purwodadi, Surabaya, Gempol, Makassar, and Cibodas between September 2012 to January 2013 (Table 1). Samples were properly authenticated by Department of Pharmacognosy and Phytochemistry, Airlangga University, Surabaya. Mature, dark green leaves of five different plants were collected from each location in triplicate; the leaves were air-dried and powdered. Moisture contents (MC) of the samples were 9.6 ± 1.7%, n = 105 (by using Moisture Analyzer HB43-S, Mettler Toledo). The maximum permitted level of MC of the herbal medicine was 12%, w/w [31].
Soil collection was performed by using composite sampling. Fifteen sub-samples were collected randomly 6-8 inches from the surface [32].

Preparation of Extracts
Two ml of methanol containing 0.1% formic acid was added to 20.0 mg of dried leaf powder. The samples were vortexed for 15 s, sonicated for 2 min, then vortexed again for 2 min, and followed by centrifugation at 13,000 x g for 10 min. The extraction process was repeated three times. Supernatants were collected and dried using N 2 . Two ml of 50% methanol was added to the residues and vortexed for 1 min before injection into the UPLC.  The proposed molecular formula was performed using SmartFormula based on the exact mass and isotopic pattern; proposed fragmentation of the compound was generated using SmartFormula 3D. Then, the fragmentation pattern of the compounds were compared with some databases using Fragmentation Explorer.

Analytical Method Validation
Stability and validation (intraday and interday precisions) were performed by injecting sample C at 0 h, 9 h, 18 h, 27 h, and 36 h in triplicate. PCA analysis confirmed that the extract was stable in 36 h, and showed acceptable intra-and interday precision (data not shown).
For checking the reliability of the method for each series of experiments, the QC sample was injected three times at the beginning of the analysis, then regularly every 8-9 samples. The coefficient variation (CV) of the data set were evaluated according to the published method [34]; our data showed that > 71.50% of the bucket data had CV < 30%. The tight clustering of the QC sample in the PCA analysis showed the reliability of the method (data not shown).